Research Seminar

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Autumn Semester 2021

Date / Time

Speaker

Title

Location

19 November 2021
14:30-15:30

Nicolas Chopin
ENSAE-CREST, IPP

Event Details

Research Seminar in Statistics

Title	Sequential Monte Carlo: past, present and future
Speaker, Affiliation	Nicolas Chopin, ENSAE-CREST, IPP
Date, Time	19 November 2021, 14:30-15:30
Location	HG G 19.1
Abstract	In this talk, I will present an overview of Sequential Monte Carlo (SMC) methods, their original motivation (filtering), their current applications in many areas of science (robotics, epidemiology, social sciences, to name a few), and the challenges that remain to be addressed to make them fully usable in certain areas. I will make a distinction between particle filters, which are SMC algorithms used to perform sequential inference in hidden Markov models, and SMC samplers, a more recent class of algorithms which may be used to approximate one, or several arbitrary distributions.

Sequential Monte Carlo: past, present and futureread_more

HG G 19.1

19 November 2021
16:00-17:00

Antonietta Mira
Università della Svizzera italiana

Event Details

Research Seminar in Statistics

Title	The ABC of ABC
Speaker, Affiliation	Antonietta Mira, Università della Svizzera italiana
Date, Time	19 November 2021, 16:00-17:00
Location	HG G 19.1
Abstract	The goal of statistical inference is to draw conclusions about properties of a population given a finite observed sample. This typically proceeds by first specifying a parametric statistical model (that identifies a likelihood function) for the data generating process which is indexed by parameters that need to be calibrated (estimated). There is always a trade-off between model simplicity / inferencial effort / prediction power. When we want to work with a realistic model, the likelihood function may not be analytically available, for example because it involves complex integrals besides the ones needed to compute normalizing constants. Still we can retain the ability to simulate pseudo samples from the model once a set or parameter values has been specified. These simulator-based models are very natural in several contexts such as Astrophysics, Neuroscience, Econometrics, Epidemiology, Ecology, Genetics and so on. When a simulator-based model is available we can rely on Approximate Bayesian Computation (ABC) to calibrate it. Indeed, ABC is a class of algorithms which has been developed to perform statistical inference (from point estimation all the way to hypothesis testing, model selection and prediction) in the absence of a likelihood function but in a setting where there exists a data generating mechanism able to return pseudo-samples. In this talk I will introduce the basic idea behind ABC and explain some of the algorithms useful for statistical inference including the simulated annealing approach by C. Albert, HR Künsch and A Scheidegger (2014). I will conclude with an example related to epidemiological models for Covid-19 data.

The ABC of ABCread_more

HG G 19.1

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